DBMS > Atos Standard Common Repository vs. Cloudflare Workers KV vs. Google Cloud Bigtable vs. Microsoft Access vs. RocksDB

System Properties Comparison Atos Standard Common Repository vs. Cloudflare Workers KV vs. Google Cloud Bigtable vs. Microsoft Access vs. RocksDB

Editorial information provided by DB-Engines

Name

This system has been discontinued and will be removed from the DB-Engines ranking.

Description

Highly scalable database system, designed for managing session and subscriber data in modern mobile communication networks

A global, low-latency, key-value store for applications on Cloudflare with exceptionally high read volumes and low-latency.

Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.

Microsoft Access combines a backend RDBMS (JET / ACE Engine) with a GUI frontend for data manipulation and queries.

The Access frontend is often used for accessing other datasources (DBMS, Excel, etc.)

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

Primary database model

Document store
Key-value store

Key-value store

Key-value store
Wide column store

Relational DBMS

Key-value store

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	0.39
Rank	#274	Overall
	#39	Key-value stores

Score	3.15
Rank	#95	Overall
	#14	Key-value stores
	#8	Wide column stores

Score	101.16
Rank	#11	Overall
	#8	Relational DBMS

Score	3.41
Rank	#86	Overall
	#11	Key-value stores

Website

atos.net/en/convergence-creators/portfolio/standard-common-repository

www.cloudflare.com/developer-platform/workers-kv

cloud.google.com/bigtable

www.microsoft.com/en-us/microsoft-365/access

rocksdb.org

Technical documentation

developers.cloudflare.com/kv/api

cloud.google.com/bigtable/docs

developer.microsoft.com/en-us/access

github.com/facebook/rocksdb/wiki

Developer

Atos Convergence Creators

Cloudflare

Google

Microsoft

Facebook, Inc.

Initial release

2016

2018

2015

1992

2013

Current release

1703

1902 (16.0.11328.20222), March 2019

9.2.1, May 2024

License

Commercial or Open Source

commercial

Bundled with Microsoft Office

Open Source

BSD

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

C++

Server operating systems

Linux

hosted

Windows

Not a real database server, but making use of DLLs

Linux

Data scheme

Schema and schema-less with LDAP views

schema-free

yes

schema-free

Typing

predefined data types such as float or date

optional

yes

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

yes

Secondary indexes

yes

SQL

Support of SQL

yes

but not compliant to any SQL standard

APIs and other access methods

LDAP

HTTP REST
Proprietary protocol

gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)

ADO.NET
DAO
ODBC
OLE DB

C++ API
Java API

Supported programming languages

All languages with LDAP bindings

C
C++
Dart
JavaScript
Kotlin
Python
Rust
Scala

C#
C++
Go
Java
JavaScript (Node.js)
Python

C
C#
C++
Delphi
Java (JDBC-ODBC)
VBA
Visual Basic.NET

C
C++
Go
Java
Perl
Python
Ruby

Server-side scripts

Stored procedures

yes

since Access 2010 using the ACE-engine

Triggers

yes

since Access 2010 using the ACE-engine

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

Sharding

none

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Internal replication in Colossus, and regional replication between two clusters in different zones

none

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on configuration

Eventual Consistency

Immediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)

Foreign keys

Referential integrity

yes

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

Atomic execution of specific operations

Atomic single-row operations

ACID

but no files for transaction logging

yes

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

but no files for transaction logging

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

User concepts

Access control

LDAP bind authentication

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

a simple user-level security was built in till version Access 2003

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